In one aspect, this specification concerns techniques for recognizing an image (or a video) as one of many stored in a database. The techniques can also be used for recognizing objects in images.
A basic concept behind many of these techniques is representing an image (or object) in terms of simple features that are either invariant to geometric transformations, change of view, noise, occlusions, background, luminance and lighting changes, or that vary slowly with these effects. One such representation is the 3D Color Histogram (c.f., Swain and Ballard, “Color Indexing,” International Journal of Computer Vision, 7(1):11-32, 1991).
Color histograms can be computed relatively quickly and have been widely used for recognition and indexing tasks. However, traditional color histograms suffer from a variety of shortcomings, such as sensitivity to brightness, contrast and luminance changes, and change in illumination.
Aspects of the present technology concern extension of color histogram concepts to create simple representations that are less sensitive to such effects. In addition, simple matching techniques are disclosed, based on histogram parameters and set theory, to provide better robustness under geometric transformations. Also detailed are techniques using histogram representations for quick database search and reduction of search space.
The detailed technology is well suited for operation on mobile devices or embedded systems, mainly due to their simplicity and speed.
The foregoing will be more readily apparent from the following detailed description, which proceeds by reference to the accompanying drawings.